{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KNN_Project.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPtylWEHwJslRTEV8k+JBGS",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/KNN_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Li6ZkvC2AAAQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLlnBrE7ARav",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('/content/KNN_Project_Data')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3l3VVOnaA02o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fcc8dc32-ed53-45e5-9741-bf575322f77e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>XVPM</th>\n",
              "      <th>GWYH</th>\n",
              "      <th>TRAT</th>\n",
              "      <th>TLLZ</th>\n",
              "      <th>IGGA</th>\n",
              "      <th>HYKR</th>\n",
              "      <th>EDFS</th>\n",
              "      <th>GUUB</th>\n",
              "      <th>MGJM</th>\n",
              "      <th>JHZC</th>\n",
              "      <th>TARGET CLASS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1636.670614</td>\n",
              "      <td>817.988525</td>\n",
              "      <td>2565.995189</td>\n",
              "      <td>358.347163</td>\n",
              "      <td>550.417491</td>\n",
              "      <td>1618.870897</td>\n",
              "      <td>2147.641254</td>\n",
              "      <td>330.727893</td>\n",
              "      <td>1494.878631</td>\n",
              "      <td>845.136088</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1013.402760</td>\n",
              "      <td>577.587332</td>\n",
              "      <td>2644.141273</td>\n",
              "      <td>280.428203</td>\n",
              "      <td>1161.873391</td>\n",
              "      <td>2084.107872</td>\n",
              "      <td>853.404981</td>\n",
              "      <td>447.157619</td>\n",
              "      <td>1193.032521</td>\n",
              "      <td>861.081809</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1300.035501</td>\n",
              "      <td>820.518697</td>\n",
              "      <td>2025.854469</td>\n",
              "      <td>525.562292</td>\n",
              "      <td>922.206261</td>\n",
              "      <td>2552.355407</td>\n",
              "      <td>818.676686</td>\n",
              "      <td>845.491492</td>\n",
              "      <td>1968.367513</td>\n",
              "      <td>1647.186291</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1059.347542</td>\n",
              "      <td>1066.866418</td>\n",
              "      <td>612.000041</td>\n",
              "      <td>480.827789</td>\n",
              "      <td>419.467495</td>\n",
              "      <td>685.666983</td>\n",
              "      <td>852.867810</td>\n",
              "      <td>341.664784</td>\n",
              "      <td>1154.391368</td>\n",
              "      <td>1450.935357</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1018.340526</td>\n",
              "      <td>1313.679056</td>\n",
              "      <td>950.622661</td>\n",
              "      <td>724.742174</td>\n",
              "      <td>843.065903</td>\n",
              "      <td>1370.554164</td>\n",
              "      <td>905.469453</td>\n",
              "      <td>658.118202</td>\n",
              "      <td>539.459350</td>\n",
              "      <td>1899.850792</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          XVPM         GWYH  ...         JHZC  TARGET CLASS\n",
              "0  1636.670614   817.988525  ...   845.136088             0\n",
              "1  1013.402760   577.587332  ...   861.081809             1\n",
              "2  1300.035501   820.518697  ...  1647.186291             1\n",
              "3  1059.347542  1066.866418  ...  1450.935357             0\n",
              "4  1018.340526  1313.679056  ...  1899.850792             0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cw7FU-1tB5Fz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "outputId": "785c4aff-5dd8-4284-8477-a5649d6e51af"
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.set_style('whitegrid')\n",
        "sns.heatmap(df.corr(),annot=True)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c2a90cb00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0US-D80hB-FX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# sns.pairplot(df,hue='TARGET CLASS')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tP-xtx-OCRpA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tn7h9utEE4Xd",
        "colab_type": "text"
      },
      "source": [
        "#The main catch in KNN is that since the method is Position based, we gotta make sure we do the appropriate FeatureScaling otherwise some features will be far off due to their magnitudes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7JWeAjrFHdf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "scaler.fit(df.drop('TARGET CLASS',axis=1))\n",
        "scaled_features = scaler.transform(df.drop('TARGET CLASS',axis=1))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6R67R9x6FS4j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a3e6eeb0-bb9c-43f1-debd-f91ce19f68c8"
      },
      "source": [
        "df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])\n",
        "df_feat.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>XVPM</th>\n",
              "      <th>GWYH</th>\n",
              "      <th>TRAT</th>\n",
              "      <th>TLLZ</th>\n",
              "      <th>IGGA</th>\n",
              "      <th>HYKR</th>\n",
              "      <th>EDFS</th>\n",
              "      <th>GUUB</th>\n",
              "      <th>MGJM</th>\n",
              "      <th>JHZC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.568522</td>\n",
              "      <td>-0.443435</td>\n",
              "      <td>1.619808</td>\n",
              "      <td>-0.958255</td>\n",
              "      <td>-1.128481</td>\n",
              "      <td>0.138336</td>\n",
              "      <td>0.980493</td>\n",
              "      <td>-0.932794</td>\n",
              "      <td>1.008313</td>\n",
              "      <td>-1.069627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.112376</td>\n",
              "      <td>-1.056574</td>\n",
              "      <td>1.741918</td>\n",
              "      <td>-1.504220</td>\n",
              "      <td>0.640009</td>\n",
              "      <td>1.081552</td>\n",
              "      <td>-1.182663</td>\n",
              "      <td>-0.461864</td>\n",
              "      <td>0.258321</td>\n",
              "      <td>-1.041546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.660647</td>\n",
              "      <td>-0.436981</td>\n",
              "      <td>0.775793</td>\n",
              "      <td>0.213394</td>\n",
              "      <td>-0.053171</td>\n",
              "      <td>2.030872</td>\n",
              "      <td>-1.240707</td>\n",
              "      <td>1.149298</td>\n",
              "      <td>2.184784</td>\n",
              "      <td>0.342811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.011533</td>\n",
              "      <td>0.191324</td>\n",
              "      <td>-1.433473</td>\n",
              "      <td>-0.100053</td>\n",
              "      <td>-1.507223</td>\n",
              "      <td>-1.753632</td>\n",
              "      <td>-1.183561</td>\n",
              "      <td>-0.888557</td>\n",
              "      <td>0.162310</td>\n",
              "      <td>-0.002793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.099059</td>\n",
              "      <td>0.820815</td>\n",
              "      <td>-0.904346</td>\n",
              "      <td>1.609015</td>\n",
              "      <td>-0.282065</td>\n",
              "      <td>-0.365099</td>\n",
              "      <td>-1.095644</td>\n",
              "      <td>0.391419</td>\n",
              "      <td>-1.365603</td>\n",
              "      <td>0.787762</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       XVPM      GWYH      TRAT  ...      GUUB      MGJM      JHZC\n",
              "0  1.568522 -0.443435  1.619808  ... -0.932794  1.008313 -1.069627\n",
              "1 -0.112376 -1.056574  1.741918  ... -0.461864  0.258321 -1.041546\n",
              "2  0.660647 -0.436981  0.775793  ...  1.149298  2.184784  0.342811\n",
              "3  0.011533  0.191324 -1.433473  ... -0.888557  0.162310 -0.002793\n",
              "4 -0.099059  0.820815 -0.904346  ...  0.391419 -1.365603  0.787762\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_IwR92aFbGK",
        "colab_type": "text"
      },
      "source": [
        "##Splitting and Training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJjMmwdIFXJg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6xGFiqKFmkN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.drop('TARGET CLASS',axis=1)\n",
        "y = df['TARGET CLASS']"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwHffMC1FuBT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZY-Cbm1CF4_V",
        "colab_type": "text"
      },
      "source": [
        "##Using **KNN**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jz84QB4nF2lO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTYbOvzBGEAM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=1)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Q5uHqkYGG2Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "4ca9f971-fedd-4985-dd5b-0cce42da2010"
      },
      "source": [
        "knn.fit(X_train,y_train)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxSgTrRyGMRX",
        "colab_type": "text"
      },
      "source": [
        "##Predictions and Evaluations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbQyj6YPGJdC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_p = knn.predict(X_test)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o8rbLZuGXRk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyd9kKvPGa40",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "68d0f61e-7a8a-489e-b9c6-9c36fef151ee"
      },
      "source": [
        "\n",
        "print(confusion_matrix(y_test,y_p))\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[109  37]\n",
            " [ 40 114]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSkEe0nRGew6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "a93b33da-c6f2-4667-e147-de4f59a77586"
      },
      "source": [
        "print(classification_report(y_test,y_p))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.75      0.74       146\n",
            "           1       0.75      0.74      0.75       154\n",
            "\n",
            "    accuracy                           0.74       300\n",
            "   macro avg       0.74      0.74      0.74       300\n",
            "weighted avg       0.74      0.74      0.74       300\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRJSyr44Gl8f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Pretty low scores. Let's optimize the scene."
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AUgkVuEiHOA7",
        "colab_type": "text"
      },
      "source": [
        "#Picking a K-Value (No. Of Neighbours)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tsw7uSLGqan",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "error_rate = []\n",
        "\n",
        "# Will take some time\n",
        "for i in range(1,40):\n",
        "    \n",
        "    knn = KNeighborsClassifier(n_neighbors=i)\n",
        "    knn.fit(X_train,y_train)\n",
        "    pred_i = knn.predict(X_test)\n",
        "    error_rate.append(np.mean(pred_i != y_test))"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTaoVD_CHV3w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "84eeaae0-a3a4-486d-cb3e-c66ea38483bf"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', marker='o',\n",
        "         markerfacecolor='red', markersize=10)\n",
        "plt.title('Error Rate vs. K Value')\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('Error Rate')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Error Rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GyqEUORGHgX9",
        "colab_type": "text"
      },
      "source": [
        "Looks Like K = 32 gives the minimum error."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDy1pWkXHaO-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "9fb8cb60-d7e3-487a-ce12-6de913fde4a0"
      },
      "source": [
        "# NOW WITH K=32\n",
        "knn = KNeighborsClassifier(n_neighbors=32)\n",
        "\n",
        "knn.fit(X_train,y_train)\n",
        "pred = knn.predict(X_test)\n",
        "\n",
        "print('WITH K=30')\n",
        "print('\\n')\n",
        "print(confusion_matrix(y_test,pred))\n",
        "print('\\n')\n",
        "print(classification_report(y_test,pred))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WITH K=30\n",
            "\n",
            "\n",
            "[[124  22]\n",
            " [ 34 120]]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.85      0.82       146\n",
            "           1       0.85      0.78      0.81       154\n",
            "\n",
            "    accuracy                           0.81       300\n",
            "   macro avg       0.81      0.81      0.81       300\n",
            "weighted avg       0.82      0.81      0.81       300\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nt5xCOpTIIf_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Improved Scores."
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWfTwSDWIOyV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}